Skip to main content
Log in

Assessment of the maintenance cost and analysis of availability measures in a finite life cycle for a system subject to competing failures

  • Regular Article
  • Published:
OR Spectrum Aims and scope Submit manuscript

Abstract

This paper deals with the assessment of the maintenance cost and the performance of a system under a finite planning horizon. The system is subject to two dependent causes of failure: internal degradation and sudden shocks. We assume that internal degradation follows a gamma process. When the deterioration level of the degradation process exceeds a threshold, a degradation failure occurs. Sudden shocks arrive at the system following a doubly stochastic Poisson process (DSPP). A sudden shock provokes the system failure. A condition-based maintenance (CBM) with periodic inspection times is implemented. Recursive methods combining numerical integration and Monte Carlo simulation are developed to evaluate the expected cost rate and its standard deviation. Also, recursive methods to calculate the reliability, the availability and the interval reliability of the system are given. Numerical examples are provided to illustrate the analytical results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63:135–149

    Article  Google Scholar 

  • Ahmadi R (2014) A new approach to modeling condition-based maintenance for stochastically deteriorating systems. Int J Reliab Qual Saf Eng 21(5):1450024

    Article  Google Scholar 

  • Asadzadeh SM, Azadeh A (2014) An integrated systemic model for optimization of condition-based maintenance with human error. Reliab Eng Syst Saf 124:117–131

    Article  Google Scholar 

  • Bagdonavičius V, Bikelis A, Kazakevicius V (2004) Statistical analysis of linear degradation and failure time data with multiple failure modes. Lifetime Data Anal 10:65–81

    Article  Google Scholar 

  • Bagdonavičius V, Bikelis A, Kazakevičius V, Nikulin M (2007) Analysis of joint multiple failure mode and linear degradation data with renewals. J Stat Plan Inference 137(7):2191–2207

    Article  Google Scholar 

  • Bertoin J (1998) Lévy processes, Cambridge tracts in mathematics. Cambridge University Press, Cambridge

    Google Scholar 

  • Caballé NC, Castro IT (2017) Analysis of the reliability and the maintenance cost for finite life cycle systems subject to degradation and shocks. Appl Math Model 52:731–746

    Article  Google Scholar 

  • Caballé NC, Castro IT, Pérez CJ, Lanza-Gutiérrez JM (2015) A condition-based maintenance of a dependent degradation-threshold-shock model in a system with multiple degradation processes. Reliab Eng Syst Saf 134:98–109

    Article  Google Scholar 

  • Castro IT, Caballé NC, Pérez CJ (2015) A condition-based maintenance for a system subject to multiple degradation processes and external shocks. Int J Syst Sci 46(9):1692–1704

    Article  Google Scholar 

  • Castro IT, Mercier S (2016) Performance measures for a deteriorating system subject to imperfect maintenance and delayed repairs. In: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 1–22 http://dx.doi.org/10.1177/1748006X16641789 10.1177/1748006X16641789

  • Cha JH, Sangüesa C, Castro IT (2016) Maintenance policy for a system with stochastically dependent failure modes with shock-accumulation effect. IEEE Trans Reliab 65(3):1284–1297

    Article  Google Scholar 

  • Cheng T, Pandey MD, van der Weide J (2012) The probability distribution of maintenance cost of a system affected by the gamma process of degradation: finite time solution. Reliab Eng Syst Saf 108:65–76

    Article  Google Scholar 

  • Fan M, Zeng Z, Zio E, Kang R (2017) Modeling dependent competing failure processes with degradation-shock dependence. Reliab Eng Syst Saf 165:422–430

    Article  Google Scholar 

  • Fouladirad M, Grall A (2011) Condition-based maintenance for a system subject to a non-homogeneous wear process with a wear rate transition. Reliab Eng Syst Saf 96(6):611–618

    Article  Google Scholar 

  • Huynh KT, Barros A, Bérenguer C, Castro IT (2011) A periodic inspection and replacement policy for systems subject to competing failure modes due to degradation and traumatic events. Reliab Eng Syst Saf 96(4):497–508

    Article  Google Scholar 

  • Huynh KT, Castro IT, Barros A, Bérenguer C (2012) Modeling age-based maintenance strategies with minimal repairs for systems subject to competing failure modes due to degradation and shocks. Eur J Oper Res 218(1):140–151

    Article  Google Scholar 

  • Korving H, Clemens F, van Noortwijk JM (2006) Statistical Modeling of the serviceability of sewage pumps. J Hydraul Eng 132(10):1076–1085

    Article  Google Scholar 

  • Li W, Pham H (2011) A condition-based maintenance model for periodically inspected systems subjected to competing failure processes. Int J Syst Assur Eng Manag 2(3):226–233

    Article  Google Scholar 

  • Li W, Pham H (2012) A condition-based inspection-maintenance model based on geometric sequences for systems with a degradation process and random shocks. Life Cycle Reliab Saf Eng 1(1):26–34

    Google Scholar 

  • Limnios N (2012) Reliability measures of semi-markov systems with general state space. Methodol Comput Appl Probab 14(4):895–917

    Article  Google Scholar 

  • Moran PAP (1954) A probability theory of dams and storage systems. Aust J App Sci 5(2):116–124

    Google Scholar 

  • Pandey M, Cheng T, van der Weide J (2011) Finite-time maintenance cost analysis of engineering systems affected by stochastic degradation. Proc Inst Mech Eng Part O J Risk Reliab 225(2):241–250

    Google Scholar 

  • Rafiee K, Feng Q, Coit DW (2015) Condition-based maintenance for repairable deteriorating systems subject to a generalized mixed shock model. IEEE Trans Reliab 64(4):1164–1174

    Article  Google Scholar 

  • Singpurwalla ND (1995) Survival in dynamic environments. Stat Sci 10(1):86–103

    Article  Google Scholar 

  • Taghipour S, Banjevic D, Jardine AK (2010) Periodic inspection optimization model for a complex repairable system. Reliab Eng Syst Saf 95(9):944–952

    Article  Google Scholar 

  • van Noortwijk J (2009) A survey of the application of gamma processes in maintenance. Reliab Eng Syst Saf 94(1):2–21

    Article  Google Scholar 

  • Wu J, Shen T, Xie M, Huang HZ (2010) Analysis of maintenance policies for finite life-cycle multi-state systems. Comput Ind Eng 59(4):638–646

    Article  Google Scholar 

  • Ye ZH, Tang LC, Xu HY (2011) A distribution-based systems reliability model under extreme shocks and natural degradation. Reliab IEEE Trans Reliab 60(1):246–256

    Article  Google Scholar 

  • Zhao X, Fouladirad M, Bérenguer C, Bordes L (2010) Condition-based inspection/replacement policies for non-monotone deteriorating systems with environmental covariates. Reliab Eng Syst Saf 95(8):921–934

    Article  Google Scholar 

  • Zhu Q, Peng H, van Houtum GJ (2015) A condition-based maintenance policy for multi-component systems with a high maintenance setup. OR Spectr 37:1007–1035

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank two anonymous referees and associate editor for comments and suggestions which have improved the paper. This research was supported by Ministerio de Economía y Competitividad, Spain (Project MTM2015-63978-P), Gobierno de Extremadura, Spain (Project GR15106) and European Union (European Regional Development Funds). Funding for a Ph.D. grant comes from Fundación Valhondo (Spain).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. T. Castro.

Appendices

Appendix A

For \(t<T\), \(E\left[ C(t)\right] \) is given by

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)\right] &{}=C_d E\left[ (t-Y) \mathbf {1}_{\{\sigma _{M_s}<Y<t,~Y<\sigma _L\}}\right] +C_d E\left[ (t-\sigma _L) \mathbf {1}_{\{\sigma _{M_s}<\sigma _L<t,~\sigma _L<Y\}}\right] \\ &{}\quad +C_d E\left[ (t-Y) \mathbf {1}_{\{Y<t,~Y<\sigma _{M_s}\}}\right] .\\ \end{aligned} \end{array} \end{aligned}$$

That is,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)\right] &{}=C_d\displaystyle \int _{0}^{t}f_{\sigma _{M_s}}(u)\int _{u}^{t}\left[ -\frac{\partial }{\partial v}I(u,v)\right] {\bar{F}}_{\sigma _{L}-\sigma _{M_s}}(v-u)(t-v)~dv~du\\ &{}\quad +C_d\displaystyle \int _{0}^{t}f_{\sigma _{M_s}}(u)\int _{u}^{t}I(u,v)f_{\sigma _{L}-\sigma _{M_s}}(v-u)(t-v)~\mathrm{d}v~\mathrm{d}u\\ &{}\quad +C_d\displaystyle \int _{0}^{t}f_1(u){\bar{F}}_{\sigma _{M_s}}(u)(t-u)\mathrm{d}u.\\ \end{aligned} \end{array} \end{aligned}$$

For \(t\ge T\), \(E\left[ C(t)\right] \) is conditioned to D

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)\right]&=E\left[ C(t),D\le t\right] +E\left[ C(t),D> t\right] . \end{aligned} \end{array} \end{aligned}$$

Thus, if \(D>t\)

$$\begin{aligned} \begin{array}{l} \begin{aligned} E \left[ C(t),D> t\right] &{}=\lfloor t/T\rfloor C_I\Bigg (1-\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }P_{D}(kT)\Bigg ) \\ &{}\quad +C_dE\left[ W(\lfloor t/T\rfloor T,t)\right] \Bigg (1-\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }P_{D}(kT)\Bigg ). \end{aligned} \end{array}\end{aligned}$$

If \(D\le t\), \(E\left[ C(t)\right] \) can be split into two terms: the cost in the first renewal cycle \(\left( C(D)\right) \) and the cost in the remaining time horizon \(\left( C(D,t)\right) \). Since C(D) and C(Dt) are independent, we get

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t),D\le t\right]&=E\left[ C(D),D\le t\right] +E\left[ C(D,t),D\le t\right] . \end{aligned} \end{array} \end{aligned}$$

Hence,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(D),D\le t\right] &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }E\left[ C(D),D=kT\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor } \left( C_c+C_I(k-1)+C_d E\left[ W((k-1)T,kT)\right] \right) P_{D,c}(kT) \\ &{}\quad +\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }\left( C_p+C_I(k-1)\right) P_{D,p}(kT). \end{aligned} \end{array} \end{aligned}$$

Since C(Dt) is stochastically the same as \(C(t-D)\),

$$\begin{aligned} E\left[ C(D,t),D=kT\right] =E\left[ C(t-kT)\right] P_{D}(kT). \end{aligned}$$

Hence, \(E\left[ C(t)\right] \) verifies the following recursive equation:

$$\begin{aligned}E\left[ C(t)\right] =\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }E\left[ C(t-kT)\right] P_{D}(kT)+G(t),\end{aligned}$$

being

$$\begin{aligned} \begin{array}{l} \begin{aligned} G(t)&{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }\Big (C_p+C_I(k-1)\Big )P_{D,p}(kT)\\ &{}\quad +\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }\Big (C_c+C_I(k-1)+C_dE\left[ W((k-1)T,kT)\right] \Big )P_{D,c}(kT)\\ &{}\quad + \left( \lfloor t/T\rfloor C_I +C_dE\left[ W(\lfloor t/T\rfloor T,t)\right] \right) \Big (1-\sum _{k=1}^{\lfloor t/T\rfloor } P_{D}(kT)\Big ),\\ \end{aligned} \end{array} \end{aligned}$$

and the result holds.

Appendix B

For \(t<T\), the expected square cost, \(E\left[ C(t)^2\right] \), is given by

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)^2\right] &{}=C_d^2 E\left[ (t-Y)^2 \mathbf {1}_{\{\sigma _{M_s}<Y<t,~Y<\sigma _L\}}\right] +C_d^2 E\left[ (t-\sigma _L)^2 \mathbf {1}_{\{\sigma _{M_s}<\sigma _L<t,~\sigma _L<Y\}}\right] \\ &{}\quad +C_d^2 E\left[ (t-Y)^2 \mathbf {1}_{\{Y<t,~Y<\sigma _{M_s}\}}\right] .\\ \end{aligned} \end{array} \end{aligned}$$

That is,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)\right] =&{}C_d^2\displaystyle \int _{0}^{t}f_{\sigma _{M_s}}(u)\int _{u}^{t}\left[ -\frac{\partial }{\partial v}I(u,v)\right] {\bar{F}}_{\sigma _{L}-\sigma _{M_s}}(v-u)(t-v)^2~\mathrm{d}v~\mathrm{d}u\\ &{}\quad +C_d^2\displaystyle \int _{0}^{t}f_{\sigma _{M_s}}(u)\int _{u}^{t}I(u,v)f_{\sigma _{L}-\sigma _{M_s}}(v-u)(t-v)^2~\mathrm{d}v~\mathrm{d}u\\ &{}\quad + C_d^2\displaystyle \int _{0}^{t}f_1(u){\bar{F}}_{\sigma _{M_s}}(u)(t-u)^2\mathrm{d}u.\\ \end{aligned} \end{array} \end{aligned}$$

For \(t\ge T\), \(E\Big [C(t)^2\Big ]\) is conditioned to D

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\left[ C(t)^2\right]&=E\left[ C(t)^2,D\le t\right] +E\left[ C(t)^2,D> t\right] . \end{aligned} \end{array} \end{aligned}$$

Hence,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E&\left[ C(t)^2,D> t\right] =\Big (\lfloor t/T\rfloor C_I +C_dE\left[ W_{T}^{M}(\lfloor t/T\rfloor T,t)\right] \Big )^2\Bigg (1-\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }P_{D}(kT)\Bigg ), \end{aligned} \end{array} \end{aligned}$$

where \(P_{D}\) is given by (9). On the other hand,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E&\left[ C(t)^2,D\le t\right] =E\left[ \left( C(D)+C(D,t)\right) ^2,D\le t\right] . \end{aligned} \end{array} \end{aligned}$$

Developing the expression

$$\begin{aligned} \begin{array}{l} \begin{aligned} E&{}\left[ \left( C(D)+C(D,t)\right) ^2,D\le t\right] =E\left[ C(D)^2,D\le t\right] +E\left[ C(D,t)^2,D\le t\right] \\ +&{}E\left[ 2~ C(D)C(D,t),D\le t\right] . \end{aligned} \end{array} \end{aligned}$$

Thus,

$$\begin{aligned} \begin{array}{l} \begin{aligned} E\Big [C(D)^2,D\le t\Big ] &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }E\left[ C(D)^2,D=kT\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }\left( C_c+C_I(k-1)+C_dE\left[ W((k-1)T,kT)\right] \right) ^2P_{D,c}(kT) \\ &{}\quad +\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }\left( C_c+C_I(k-1)\right) ^2P_{D,p}(kT). \end{aligned} \end{array} \end{aligned}$$

Following the same reasoning as in “Appendix A”,

$$\begin{aligned}&E \left[ C(D)C(D,t),D\le t\right] \\&\quad =\sum _{k=1}^{\lfloor t/T \rfloor }\left( C_c+C_I(k-1)\right) E\left[ C(t-kT)\right] P_{D,c}(kT)\\&\qquad +\sum _{k=1}^{\lfloor t/T \rfloor }C_dE\left[ W((k-1)T,kT)\right] E\left[ C(t-kT)\right] P_{D,c}(kT)\\&\qquad +\sum _{k=1}^{\lfloor t/T \rfloor }\left( C_p+C_I(k-1)\right) E\left[ C(t-kT)\right] P_{D,p}(kT). \end{aligned}$$

Hence, \(E\left[ C(t)^2\right] \) verifies the following recursive equation:

$$\begin{aligned} E\left[ C(t)^2\right] =\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }E\left[ C(t-kT)^2\right] P_{D}(kT)+H(t), \end{aligned}$$

being

$$\begin{aligned} H(t)&=\displaystyle \sum _{k=1}^{\lfloor t/T \rfloor } \left( C_p+C_I(k-1)\right) ^2P_{D,p}(kT)\\&\quad +\displaystyle \sum _{k=1}^{\lfloor t/T \rfloor } \left( C_c+C_I(k-1)+C_dE\left[ W((k-1)T,kT)\right] \right) ^2P_{D,c}(kT)\\&\quad +2\sum _{k=1}^{\lfloor t/T \rfloor }\left( C_c+C_I(k-1)\right) E\left[ C(t-kT)\right] P_{D,c}(kT)\\&\quad +2\sum _{k=1}^{\lfloor t/T \rfloor }C_dE\left[ W((k-1)T,kT)\right] E\left[ C(t-kT)\right] P_{D,c}(kT)\\&\quad +2\sum _{k=1}^{\lfloor t/T \rfloor }\left( C_p+C_I(k-1)\right) E\left[ C(t-kT)\right] P_{D,p}(kT)\\&\quad +\Big (\lfloor t/T\rfloor C_I +C_dE\left[ W(\lfloor t/T\rfloor T,t)\right] \Big )^2\Bigg (1-\sum _{k=1}^{\lfloor t/T\rfloor }P_{D}(kT)\Bigg ), \end{aligned}$$

and the result holds.

Appendix C

For \(t<T\), A(t) is given by

$$\begin{aligned} \begin{array}{l} \begin{aligned} A(t)&{}= P\left[ t<\sigma _{M_s},~Y>t\right] +P\left[ \sigma _{M_s}<t<\sigma _L,~Y>t\right] \\ &{}={\bar{F}}_{\sigma _{M_s}}(t){\bar{F}}_{1}(t)+\displaystyle \int _{0}^{t}f_{\sigma _{M_s}}(u){\bar{F}}_{\sigma _{L}-\sigma _{M_s}}(t-u)I(u,t)\mathrm{d}u.\\ \end{aligned} \end{array} \end{aligned}$$

For \(t\ge T\), A(t) is conditioned to the time to the first renewal

$$\begin{aligned} \begin{array}{l} \begin{aligned} A(t)&{}= \displaystyle \sum _{j=0}^{\infty }\mathbf {1}_{\{R_{j}\le t<R_{j+1}\}}\Big [P\left[ O(t)<L,~Y>(t-R_j),~D\le t\right] \\ &{}\quad +P\left[ O(t)<L,~Y>(t-R_j),~D> t\right] \Big ]. \end{aligned} \end{array} \end{aligned}$$

If \(D>t\)

$$\begin{aligned} \begin{array}{l} \begin{aligned} A(t)&{}= \Big [P\left[ t<\sigma _{M},~Y>t\right] \\ &{}+P\left[ \lfloor t/T\rfloor T<\sigma _{M}<\sigma _{M_s}< t<\sigma _{L},~Y>t\right] \\ &{}\quad +P\left[ \lfloor t/T\rfloor T<\sigma _{M}< t<\sigma _{M_s},~Y>t\right] \Big ]\mathbf {1}_{\left\{ M\le M_s\right\} }\\ &{}\quad + \Big [P\left[ t<\sigma _{M_s},~Y>t\right] \\ &{}\quad +P\left[ \sigma _{M_s}< \lfloor t/T\rfloor T<\sigma _{M}< t<\sigma _{L},~Y>t\right] \\ &{}\quad +P\left[ \sigma _{M_s}<\lfloor t/T\rfloor T< t<\sigma _{M},~Y>t\right] \\ &{}\quad +P\left[ \lfloor t/T\rfloor T<\sigma _{M_s}< t<\sigma _{L},~Y>t\right] \Big ]\mathbf {1}_{\left\{ M> M_s\right\} }. \end{aligned} \end{array} \end{aligned}$$

That is

$$\begin{aligned} \begin{array}{l} \begin{aligned} A(t)&{}= \Big [{\bar{F}}_{\sigma _{M}}(t){\bar{F}}_{1}(t)\\ &{}\quad +\displaystyle \int _{\lfloor t/T \rfloor T}^t f_{\sigma _{M}}(u)\int _{u}^{t}f_{\sigma _{M_s}-\sigma _{M}}(v-u){\bar{F}}_{\sigma _{L}-\sigma _{M_s}}(t-v)I(v,t)~dv~du\\ &{}+\displaystyle \int _{\lfloor t/T \rfloor T}^t f_{\sigma _{M}}(u){\bar{F}}_{\sigma _{M_s}-\sigma _{M}}(t-u){\bar{F}}_{1}(t)~du\Big ]\mathbf {1}_{\left\{ M\le M_s\right\} } +\Big [{\bar{F}}_{\sigma _{M_s}}(t){\bar{F}}_{1}(t)\\ &{}+\displaystyle \int _{0}^{\lfloor t/T \rfloor T} f_{\sigma _{M_s}}(u)\int _{\lfloor t/T \rfloor T}^{t}f_{\sigma _{M}-\sigma _{M_s}}(v-u){\bar{F}}_{\sigma _{L}-\sigma _{M}}(t-v)I(u,t)~dv~du\\ &{}+\displaystyle \int _{0}^{\lfloor t/T \rfloor T} f_{\sigma _{M_s}}(u){\bar{F}}_{\sigma _{M}-\sigma _{M_s}}(t-u)I(u,t)~du\\ &{}+\displaystyle \int _{\lfloor t/T \rfloor T}^t f_{\sigma _{M_s}}(u){\bar{F}}_{\sigma _{L}-\sigma _{M_s}}(t-u)I(u,t)~du\Big ]\mathbf {1}_{\left\{ M> M_s\right\} }\\ &{}=J_{T,1}^M(t)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{T,2}^M(t)\mathbf {1}_{\left\{ M> M_s\right\} }. \end{aligned} \end{array} \end{aligned}$$

If \(D\le t\),

$$\begin{aligned} \begin{array}{l} \begin{aligned} \displaystyle \sum _{j=0}^{\infty }&{}\mathbf {1}_{\{R_{j}\le t<R_{j+1}\}}P\left[ O(t)<L,~Y>(t-R_j),~D\le t\right] \\ =\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }&{}P_{D}(kT)\Bigg [ \sum _{j=0}^{\infty }\mathbf {1}_{\{R_{j}\le t<R_{j+1}\}}P\left[ O(t-kT)<L,~Y>(t-kT-R_j)\right] \Bigg ]\\ =\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }&{}A(t-kT)P_{D}(kT). \end{aligned} \end{array} \end{aligned}$$

Then, for \(t\ge T\), A(t) verifies the following recursive equation:

$$\begin{aligned} \begin{array}{l} \begin{aligned} A(t)&=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }A(t-kT)P_{D}(kT)+J_{1}(t)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{2}(t)\mathbf {1}_{\left\{ M> M_s\right\} }, \end{aligned} \end{array} \end{aligned}$$

and the result holds.

Appendix D

For \(t<T\), there is no maintenance action on [0, t]; hence, R(t) is equal to A(t).

For \(t\ge T\), R(t) is conditioned to the time of the first replacement

$$\begin{aligned} \begin{array}{l} \begin{aligned} R(t)&{}=P\left[ O(u)<L,~\forall u\in (0,t],~N_s(0,t)=0,~D\le t\right] \\ &{}\quad +P\left[ O(u)<L,~\forall u\in (0,t],~N_s(0,t)=0,~D> t\right] . \end{aligned} \end{array} \end{aligned}$$

If \(D>t\)

$$\begin{aligned} R(t)= & {} J_{1}(t)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{2}(t)\mathbf {1}_{\left\{ M> M_s\right\} }. \end{aligned}$$

If \(D\le t\),

$$\begin{aligned} \begin{array}{l} \begin{aligned} R(t)&{}=P\left[ O(u)<L,~\forall u\in (0,t],N_s(0,t)=0,~D\le t\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }P_{D,p}(kT)P\left[ O(u)<L,\forall u\in (0,t-kT],N_s(0,t-kT)=0\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }R(t-kT)P_{D,p}(kT). \end{aligned} \end{array} \end{aligned}$$

Then, for \(t\ge T\), R(t) verifies the following recursive equation:

$$\begin{aligned} \begin{array}{l} \begin{aligned} R(t)&=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }R(t-kT)P_{D,p}(kT) +J_{1}(t)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{2}(t)\mathbf {1}_{\left\{ M> M_s\right\} }, \end{aligned} \end{array} \end{aligned}$$

and the result holds.

Appendix E

For \((t+s)<T\), there is no maintenance action on \([0, t+s]\); hence, \(IR(t,t+s)\) is equal to \(R(t+s)\). For \(t+s\ge T\), \(IR(t,t+s)\) is conditioned to the time of the first replacement

$$\begin{aligned} \begin{array}{l} \begin{aligned} IR(t,t+s)&{}=P\left[ O(u)<L,\forall u\in (t,t+s], N_s(t,t+s)=0,~D\le t\right] \\ &{}\quad +P\left[ O(u)<L,~\forall u\in (t,t+s],\, N_s(t,t+s)=0,~t<D< t+s\right] \\ &{}\quad +P\left[ O(u)<L,~\forall u\in (t,t+s],\, N_s(t,t+s)=0,~D\ge t+s\right] . \end{aligned} \end{array} \end{aligned}$$

If \(D\ge t+s\)

$$\begin{aligned} \begin{array}{l} \begin{aligned} IR(t,t+s)&{}=A(t+s)\\ &{}=J_{1}(t+s)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{2}(t+s)\mathbf {1}_{\left\{ M> M_s\right\} }. \end{aligned} \end{array} \end{aligned}$$

If \(t<D<t+s\)

$$\begin{aligned} IR(t,t+s)&=P\left[ O(u)<L,~\forall u\in (t,t+s],\, N_s(t,t+s)=0,~t<D<t+s\right] \\&=\displaystyle \sum _{k=\lfloor t/T\rfloor +1}^{\lfloor (t+s)/T\rfloor }P_{D,p}(kT)P\left[ O(u-kT)<L, \, \forall u\in \right. \\&\quad \left. (0,t+s-kT],N_s(0,t+s-kT)=0\right] \\&=\displaystyle \sum _{k=\lfloor t/T\rfloor +1}^{\lfloor (t+s)/T\rfloor }R(t+s-kT)P_{D,p}(kT). \end{aligned}$$

If \(D\le t\)

$$\begin{aligned} \begin{array}{l} \begin{aligned} IR(t,t+s)&{}=P\left[ O(u)<L,\forall u\in (t,t+s],~N_s(t,t+s)=0,~D\le t\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }P_{D}(kT)P\left[ O(u-kT)<L,\forall u\in (t-kT,t+s-kT],\right. \\ &{}\quad \left. ~N_s(t-kT,t+s-kT)=0\right] \\ &{}=\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }IR(t-kT,t+s-kT)P_{D}(kT). \end{aligned} \end{array} \end{aligned}$$

Then, for \(t+s\ge T\), \(IR(t,t+s)\) verifies the following recursive equation:

$$\begin{aligned} \begin{array}{l} \begin{aligned} IR(t,t+s)&{}=\displaystyle \sum _{k=\lfloor t/T\rfloor +1}^{\lfloor (t+s)/T\rfloor }R(t+s-kT)P_{D,p}(kT)\\ &{}\quad +\displaystyle \sum _{k=1}^{\lfloor t/T\rfloor }IR(t-kT,t+s-kT)P_{D}(kT)\\ &{}\quad +J_{1}(t+s)\mathbf {1}_{\left\{ M\le M_s\right\} }+J_{2}(t+s)\mathbf {1}_{\left\{ M> M_s\right\} }, \end{aligned} \end{array} \end{aligned}$$

and the result holds.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Caballé, N.C., Castro, I.T. Assessment of the maintenance cost and analysis of availability measures in a finite life cycle for a system subject to competing failures. OR Spectrum 41, 255–290 (2019). https://doi.org/10.1007/s00291-018-0521-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00291-018-0521-7

Keywords

Navigation